There are numerous applications where video images are taken and recorded. Some of these applications involve the recording of video while a transaction is taking place, e.g., at an ATM or at a bank counter. The use of video recording is anticipated to increase significantly in the immediate future, such as in shopping centers, aboard buses and trains, and the like. Digital recording of video takes enormous amounts of recording space despite compression techniques such as MPEG, the use of slow video acquired at several frames per second rather than at a full 30 frames-per-second, and reductions in resolution. As a result, the recording times of digital recorders with multiple video inputs are still limited despite the use of large Giga and Terra Byte storage devices.
For some time now, there have been market available machine vision cameras and systems that can be programmed to detect certain geometrical objects. In general these objects have a very simple geometry such as nuts, bolts, engine parts, etc. In the case of face finding and processing the current state of the art is a slow process that requires massive computing power and hardware, often resulting in a system too complex to be reliable and manageable. Such a system, since requiring many components to be feasible, is difficult to deploy and scale.
A real-time processing system built with the current state-of-the-art would be cost prohibitive; as a compromise, system architects of these systems often trade-off complexity for performance. This typically results in small systems processing recorded images. Such systems are slow and incapable of processing images in real time. An improved system for image recognition is highly desirable.